\section{Conclusions and Future Work}

We presented a novel approach for skilled collaborative team formation based on finding dense subgraphs. On the theoretical front, we showed constant factor approximation algorithms. On the practical side, we showed several heuristic improvements to our main provable algorithm, and compared it to the previous approach based on identifying small diameter subgraphs. Our experimental results show that the densest subgraph approach significantly outperforms the previous techniques on multiple different measures of collaborative compatibility. 

However, team formation definitions can be extended along many dimensions. 
%Skills are never binary (i.e. expertise of different people for a specific skill 
%For example, while nodes are selected into a team based on multiple skills they possess, 
In reality a team's 
%skills and collaboration prospective depending 
value depends
on several more complex assets such as time constraints, 
%a node's contributions eventually are limited also by time constraints. Further, there are other assets of nodes that may contribute to a team's compatibility, such as their affiliation institutes, 
cultural backgrounds, geographical location, personalities, skill expertise measured as a range, ability to work in teams etc. 
%Some of these characteristics cannot even be measured easily.
%, and thereby make the team formation problem very complex in reality. The current formulations do not address these issues. 
Further, for both, the main algorithm that we presented as well as the provable algorithms from Lappas et al.~\cite{LLT},
%in the worst case, have a time complexity cubic in the number of nodes. A question is to find 
it would be interesting to find better linear time algorithms.
%without sacrificing the connectivity of the resulting solution. 
%Another direction for future work is to see how different techniques perform when the number of skills involved in the graph is larger. 
%
While the current models are a good start, these additional measures and time-complexities leave several questions open for further study in order to move closer to the motivating realistic scenario.

%\noindent {\bf Acknowledgements.} We would like to thank anonymous reviewers for helpful comments.